{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['NUMPY_EXPERIMENTAL_ARRAY_FUNCTION'] = '0'\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import gcsfs\n",
    "import core\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = 12, 6\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>pr</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>prsn</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tasmax</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AerChemMIP</td>\n",
       "      <td>BCC</td>\n",
       "      <td>BCC-ESM1</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tasmin</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id source_id experiment_id member_id table_id  \\\n",
       "0  AerChemMIP            BCC  BCC-ESM1        ssp370  r1i1p1f1     Amon   \n",
       "1  AerChemMIP            BCC  BCC-ESM1        ssp370  r1i1p1f1     Amon   \n",
       "2  AerChemMIP            BCC  BCC-ESM1        ssp370  r1i1p1f1     Amon   \n",
       "3  AerChemMIP            BCC  BCC-ESM1        ssp370  r1i1p1f1     Amon   \n",
       "4  AerChemMIP            BCC  BCC-ESM1        ssp370  r1i1p1f1     Amon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0          pr         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...   \n",
       "1        prsn         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...   \n",
       "2         tas         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...   \n",
       "3      tasmax         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...   \n",
       "4      tasmin         gn  gs://cmip6/AerChemMIP/BCC/BCC-ESM1/ssp370/r1i1...   \n",
       "\n",
       "   dcpp_init_year  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://storage.googleapis.com/pangeo-cmip6/pangeo-cmip6-zarr-consolidated-stores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_theta = df[(df.table_id == 'Omon') & (df.variable_id == 'thetao')]\n",
    "uri = df_theta[(df_theta.source_id == 'SAM0-UNICON') &\n",
    "                         (df_theta.experiment_id == 'historical')].zstore.values[0]\n",
    "gcs = gcsfs.GCSFileSystem(token='anon')\n",
    "ds_theta = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)\n",
    "df_salt = df[(df.table_id == 'Omon') & (df.variable_id == 'so')]\n",
    "uri = df_salt[(df_salt.source_id == 'SAM0-UNICON') &\n",
    "                         (df_salt.experiment_id == 'historical')].zstore.values[0]\n",
    "gcs = gcsfs.GCSFileSystem(token='anon')\n",
    "ds_salt = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)\n",
    "df_v = df[(df.table_id == 'Omon') & (df.variable_id == 'vo')]\n",
    "uri = df_v[(df_v.source_id == 'SAM0-UNICON') &\n",
    "                         (df_v.experiment_id == 'historical')].zstore.values[0]\n",
    "gcs = gcsfs.GCSFileSystem(token='anon')\n",
    "ds_v = xr.open_zarr(gcs.get_mapper(uri), consolidated=True)\n",
    "#run_counts = df_v.groupby(['source_id', 'experiment_id'])['zstore'].count()\n",
    "#run_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gsw\n",
    "dens=xr.apply_ufunc(gsw.density.sigma0,ds_salt['so'], ds_theta['thetao'],dask='parallelized', output_dtypes=[float, ]\n",
    "                   ).rename('dens').to_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def finegrid_metrics(levs,lev_bnds):\n",
    "    drF=np.diff(lev_bnds,axis=1)\n",
    "    drC=np.concatenate((np.array([levs[0]]),np.diff(levs,axis=0),np.array([lev_bnds[-1,-1]-levs[-1]])))\n",
    "    return(drF,drC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_drf,fine_drc=finegrid_metrics(ds_theta.lev.values,ds_theta.lev_bnds.values)\n",
    "fine_drc.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0.16666667, 0.16666667, 0.16666667, ..., 4.1662083 , 4.1662083 ,\n",
      "       4.1662083 ], dtype=float32), array([ 1,  1,  1, ..., 59, 59, 59], dtype=int32), array([1., 1., 1., ..., 0., 0., 0.], dtype=float32), array([ 1,  1,  1, ..., 60, 60, 60], dtype=int32))\n"
     ]
    }
   ],
   "source": [
    "import finegrid\n",
    "print(finegrid.finegrid(np.squeeze(fine_drf),np.squeeze(fine_drc),[fine_drf.size,10]))#np.squeeze(fine_drf).T,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1c78077748>]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 368
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drf_finer,mapindex,mapfact,cellindex=finegrid.finegrid(np.squeeze(fine_drf),np.squeeze(fine_drc),[fine_drf.size,10])\n",
    "plt.plot(cellindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import layers\n",
    "thetalayers=np.linspace(-3,31,80)\n",
    "VH=layers.layers_1(ds_v.vo[0,:,100,100].values,\n",
    "         ds_theta.thetao[0,:,100,100].values,thetalayers,mapfact,mapindex,cellindex,drf_finer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'dens' ()>\n",
       "array(5.52876887)\n",
       "Coordinates:\n",
       "    time     object 1850-01-17 00:30:00"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_in.min().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_in=ds_v.vo[0,:,:,:]#.transpose('lev','time','j','i')\n",
    "theta_in=dens.dens[0,:,:,:]#.transpose('lev','time','j','i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "thetalayers=np.linspace(20,30,80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_lay1=xr.apply_ufunc(core.layers_numpy,v_in, theta_in,#.chunk({'time': 2})\n",
    "                      kwargs={'thetalayers':thetalayers,'mapfact':mapfact,\n",
    "                                                  'mapindex':mapindex,'cellindex':cellindex,\n",
    "                                                  'drf_finer':drf_finer},\n",
    "                     dask='parallelized',input_core_dims=[['lev'],['lev']],output_core_dims=[['Tlev']],\n",
    "                     output_dtypes=[float],output_sizes={'Tlev':thetalayers.size})\n",
    "v_lay1=v_lay1.assign_coords({'Tlev':thetalayers})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1c48453f60>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 451,
       "width": 575
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Note: I have not summed with dx because I haven't had time to put it in\n",
    "fig=plt.figure(figsize=(10,7))\n",
    "(v_lay1).cumsum('Tlev').sum('i').plot(x='j',yincrease=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
